CN114358041A - Characteristic waveform extraction method and analysis method based on hybrid algorithm - Google Patents

Characteristic waveform extraction method and analysis method based on hybrid algorithm Download PDF

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CN114358041A
CN114358041A CN202111435045.8A CN202111435045A CN114358041A CN 114358041 A CN114358041 A CN 114358041A CN 202111435045 A CN202111435045 A CN 202111435045A CN 114358041 A CN114358041 A CN 114358041A
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signal
fundamental wave
frequency
characteristic waveform
sequence
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杨帆
李洋
张忠
金亚曦
周福顺
马晓璐
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MaAnshan Power Supply Co of State Grid Anhui Electric Power Co Ltd
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MaAnshan Power Supply Co of State Grid Anhui Electric Power Co Ltd
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Abstract

The invention discloses a characteristic waveform extracting method and analyzing method based on a hybrid transform algorithm, wherein the characteristic waveform comprises an instantaneous fundamental wave amplitude value a (k) corresponding to a sampling signal X (k), a singular value characteristic waveform p (k) and a frequency spectrum X only containing main frequency pointsm(k) (ii) a Carrying out fast Fourier transform on the sampling signal to obtain a sequence X (k), and filtering the obtained sequence to obtain a fundamental wave signal; the instantaneous fundamental wave amplitude value can be extracted by carrying out Hilbert transform on the fundamental wave signal; obtaining a high-frequency signal h (k) according to the fundamental wave signal l (k), and extracting a singular value characteristic waveform p (k) of the signal by using the sliding singular value decomposition; extracting frequency spectrum X only containing main frequency points from sequence X (k) by using envelope extremum algorithmm(k) (ii) a The invention considers the complex characteristics of the transient power quality disturbance signal and utilizes the corresponding algorithm to carry out different operationsThe features are extracted, and the identification degree and observability of the features are improved.

Description

Characteristic waveform extraction method and analysis method based on hybrid algorithm
Technical Field
The invention relates to data feature extraction, in particular to a feature waveform extraction method and an analysis method based on a hybrid algorithm.
Background
Electric energy is an indispensable resource for operation and development of the current society, the application level reflects the economic level of a country to a certain extent, and meanwhile, the demands of various industries and family users on the quantity and quality of the electric energy are gradually increased. In order to improve the power quality problems of broadband resonance, voltage disturbance and the like of a power system, long-term monitoring and intelligent analysis of power quality data are indispensable. How to extract effective information according to the data of the target signal for further transient disturbance detection and positioning becomes an important problem.
In recent years, scholars at home and abroad propose a plurality of feature extraction methods. The extracted features mainly include time domain features and frequency domain features, and the time domain features include signal amplitude features (root mean square, peak value, and the like), statistical features (standard deviation, harmonic mean, and the like), and energy features. The frequency domain features extracted by the FFT include fundamental amplitude, total harmonic distortion and the like. The main involved methods are different sequence transforms such as hilbert transform, wavelet transform, S-transform, etc. The extraction of single signal features is relatively mature and comprehensive, and no mature method is found for the extraction of mixed disturbance features. Meanwhile, how to extract appropriate mixed features from complex transient harmonic disturbance signals is also a big problem.
Disclosure of Invention
The invention provides a hybrid transformation algorithm characteristic waveform extraction method and an analysis method considering the complex characteristics of transient power quality disturbance signals, aiming at the problems in the prior art.
The technical scheme adopted by the invention is as follows:
a method for extracting characteristic waveform based on mixed transformation algorithm includes sampling signal x (k), corresponding instantaneous fundamental wave amplitude a (k), singular value characteristic waveform p (k) and only containing main frequencyFrequency spectrum X of the point of the ratem(k)；
The instantaneous fundamental wave amplitude a (k) is extracted as follows: carrying out fast Fourier transform on the sampling signal to obtain a sequence X (k), and filtering the obtained sequence to obtain a fundamental wave signal;
the instantaneous fundamental wave amplitude value can be extracted by carrying out Hilbert transform on the fundamental wave signal;
the singular value characteristic waveform p (k) is extracted by the following method: obtaining a high-frequency signal h (k) according to the fundamental wave signal l (k), and extracting a singular value characteristic waveform p (k) of the signal by using the sliding singular value decomposition;
frequency spectrum X containing only dominant frequency pointsm(k) The extraction method comprises the following steps: extracting frequency spectrum X only containing main frequency points from sequence X (k) by using envelope extremum algorithmm(k)；
Where k is the corresponding sampling point, k is 1,2, …, N is the signal length, and the sampling frequency is fs。
Further, the method for extracting the instantaneous fundamental wave amplitude a (k) specifically comprises the following steps:
s11: carrying out fast Fourier transform on the sampling signals to obtain a sequence X (k);
s12: FFTSHIFT conversion is carried out on the sequence X (k) to obtain a converted sequence Xs(k)；
Xs(k)＝FFTSHIFT(X(k))＝[X(k1),X(k2)]
Wherein k is 1,2, …, N-1, and N is the signal length; FFTSHIFT is a frequency alignment function;
s13: to Xs(k) Filtering to obtain a sequence G (k);
Figure BDA0003381500640000021
wherein f isLFor the cut-off frequency, floor is rounded down, Δ f ═ fsthe/N is a sampling frequency interval;
s14: reducing the frequency component position of the sequence G (k) by adopting FFTSHIFT transformation, and extracting a fundamental wave signal l (k) through fast Fourier inverse transformation;
l(k)＝IFFT(FFTSHIFT(Xs(k)·G(k)))
s15: the instantaneous fundamental wave amplitude value can be extracted by carrying out Hilbert transform on the fundamental wave signal;
a(k)＝abs(HT(l(k)))。
further, the singular value characteristic waveform p (k) extraction method specifically comprises the following steps:
s21: obtaining a high-frequency signal h (k) according to the fundamental wave signal l (k);
h(k)＝x(k)-l(k)
s22: m continuous data points are selected from the high-frequency signal H (k) to generate a Hankel matrix H with Q rows and M-Q +1 columnsk；
Figure BDA0003381500640000022
Figure BDA0003381500640000023
Wherein q iskA definition vector formed by M data points extracted from the high-frequency signal h (k);
s23: to HkSingular value decomposition is carried out to obtain singular value characteristic waveform p (k):
Figure BDA0003381500640000024
wherein: u shapekAs a diagonal matrix, ΛkAnd
Figure BDA0003381500640000025
orthogonal;
Figure BDA0003381500640000031
Figure BDA0003381500640000032
wherein the content of the first and second substances,
Figure BDA0003381500640000033
is a matrix HkThe characteristic value of (2).
Further, the frequency spectrum X only containing the main frequency pointsm(k) The extraction method comprises the following specific processes:
s31: collecting all maximum points in the sequence X (k) to form a maximum sequence | X1(j) 1,2, …, J and J are maximum point numbers;
s32: maximum envelope | X2(k) I is:
Figure BDA0003381500640000034
wherein c is 0, 1, …, dj-1；d1，d2，…，dJFrequency points separated by adjacent extreme points;
Figure BDA0003381500640000035
further, in step S13, a 100Hz low pass filter is used for Xs(k) And (6) filtering.
Adopting a waveform analysis method of the extracted characteristic waveform, adopting instantaneous fundamental wave amplitude a (k) corresponding to a sampling signal X (k), singular value characteristic waveform p (k) and frequency spectrum X only containing main frequency pointsm(k) And carrying out target signal identification and matching.
The invention has the beneficial effects that:
the invention considers the complex characteristics of the transient power quality disturbance signal, extracts different characteristics by using corresponding algorithms, and improves the identification degree and observability of the characteristics.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
FIG. 2 is an initial transient power quality signal according to an embodiment of the present invention.
Fig. 3 shows the instantaneous fundamental amplitude a (k) extracted in the embodiment of the present invention.
Fig. 4 is a singular value characteristic waveform p (k) extracted in the embodiment of the present invention.
FIG. 5 is a diagram illustrating a spectrum X extracted from an embodiment of the present invention and including only dominant frequency pointsm(k)。
Detailed Description
The invention is further described with reference to the following figures and specific embodiments.
As shown in FIG. 1, a method for extracting a signature based on a hybrid transform algorithm includes an instantaneous fundamental amplitude a (k) corresponding to a sampling signal X (k), a singular value signature p (k), and a spectrum X only including dominant frequency pointsm(k)；
The instantaneous fundamental wave amplitude a (k) is extracted as follows: and carrying out fast Fourier transform on the sampling signal to obtain a sequence X (k), and filtering the obtained sequence to obtain a fundamental wave signal. And (4) performing Hilbert transform on the fundamental wave signal to extract an instantaneous fundamental wave amplitude.
The specific process is as follows:
s11: carrying out fast Fourier transform on the sampling signals to obtain a sequence X (k);
s12: FFTSHIFT conversion is carried out on the sequence X (k) to obtain a converted sequence Xs(k)；
Xs(k)＝FFTSHIFT(X(k))＝[X(k1),X(k2)]
Wherein k is 1,2, …, N-1, and N is the signal length; FFTSHIFT is a frequency alignment function;
s13: to Xs(k) Filtering by a 100Hz low-pass filter to obtain a sequence G (k);
Figure BDA0003381500640000041
wherein f isLFor the cut-off frequency, floor is rounded down, Δ f ═ fsthe/N is a sampling frequency interval;
s14: reducing the frequency component position of the sequence G (k) by adopting FFTSHIFT transformation, and extracting a fundamental wave signal l (k) through fast Fourier inverse transformation;
l(k)＝IFFT(FFTSHIFT(Xs(k)·G(k)))
s15: the instantaneous fundamental wave amplitude value can be extracted by carrying out Hilbert transform on the fundamental wave signal;
a(k)＝abs(HT(l(k)))。
the singular value characteristic waveform p (k) is extracted by the following method: obtaining a high-frequency signal h (k) according to the fundamental wave signal l (k), and extracting a singular value characteristic waveform p (k) of the signal by using a gliding Singular Value Decomposition (SVD).
The specific process is as follows:
s21: obtaining a high-frequency signal h (k) according to the fundamental wave signal l (k);
h(k)＝x(k)-l(k)
s22: m continuous data points are selected from the high-frequency signal H (k) to generate a Hankel matrix H with Q rows and M-Q +1 columnsk；
Figure BDA0003381500640000042
Figure BDA0003381500640000051
Wherein q iskA definition vector formed by M data points extracted from the high-frequency signal h (k).
S23: to HkSingular value decomposition is carried out to obtain singular value characteristic waveform p (k):
Figure BDA0003381500640000052
wherein: u shapekAs a diagonal matrix, ΛkAnd
Figure BDA0003381500640000053
orthogonal;
Figure BDA0003381500640000054
Figure BDA0003381500640000055
wherein the content of the first and second substances,
Figure BDA0003381500640000056
is a matrix HkThe characteristic value of (2).
Frequency spectrum X containing only dominant frequency pointsm(k) The extraction method comprises the following steps: extracting frequency spectrum X only containing main frequency points from sequence X (k) by using envelope extremum algorithmm(k)；
Where k is the corresponding sampling point, k is 1,2, …, N is the signal length, and the sampling frequency is fs。
The specific process is as follows:
s31: collecting all maximum points in the sequence X (k) to form a maximum sequence | X1(j) 1,2, …, J and J are maximum point numbers;
s32: maximum envelope | X2(k) I is:
Figure BDA0003381500640000057
wherein c is 0, 1, …, dj-1；d1，d2，…，dJFrequency points separated by adjacent extreme points;
Figure BDA0003381500640000058
using sampling signal X (k) corresponding instantaneous fundamental wave amplitude a (k), singular value characteristic waveform p (k) and frequency spectrum X only containing main frequency pointsm(k) And carrying out identification and matching analysis on the target signal.
To validate the method of the invention, an oscillating transient signal was generated using a numerical simulation with MATLAB, as shown in fig. 2. To which white gaussian noise with a signal-to-noise ratio of 20dB is added.
The characteristic waveform extraction is carried out by adopting the method disclosed by the invention, and the result is shown in FIGS. 3-5. The amplitude of the instantaneous fundamental wave extracted in fig. 3 ranges from 0.9845 to 1.0177pu., so that it can be seen that no amplitude disturbance exists. Fig. 4 is a singular value signature obtained by noise processing through the sliding singular value decomposition, and it can be seen that the noise is effectively suppressed while the characteristics of the two oscillations are enhanced. Spectral feature X of sampled signal containing only dominant frequency points in FIG. 5mBesides the fundamental frequency of 50Hz, the harmonic frequency of integral multiple is also contained, which shows the characteristics of the oscillation transient signal.
The invention adopts a hybrid transformation algorithm to extract the characteristic waveform, and utilizes a Hilbert transformation, a singular value extraction method and an envelope extreme value algorithm to extract different characteristic waveforms of a sampling signal. And according to the characteristic extraction of the waveform characteristic pertinence existing in the signal, further detecting and analyzing the signal. The complex characteristics of the transient power quality disturbance signals are considered, different characteristics are extracted by using a corresponding transformation algorithm, and the identification degree and observability of the characteristics are improved.

Claims (6)

1. A characteristic waveform extraction method based on a hybrid transformation algorithm is characterized in that the characteristic waveform comprises an instantaneous fundamental wave amplitude a (k) corresponding to a sampling signal X (k), a singular value characteristic waveform p (k) and a frequency spectrum X only containing main frequency pointsm(k)；
The instantaneous fundamental wave amplitude a (k) is extracted as follows: carrying out fast Fourier transform on the sampling signal to obtain a sequence X (k), and filtering the obtained sequence to obtain a fundamental wave signal;
the instantaneous fundamental wave amplitude value can be extracted by carrying out Hilbert transform on the fundamental wave signal;
the singular value characteristic waveform p (k) is extracted by the following method: obtaining a high-frequency signal h (k) according to the fundamental wave signal l (k), and extracting a singular value characteristic waveform p (k) of the signal by using the sliding singular value decomposition;
frequency spectrum X containing only dominant frequency pointsm(k) The extraction method comprises the following steps: extracting frequency spectrum X only containing main frequency points from sequence X (k) by using envelope extremum algorithmm(k)；
Where k is the corresponding sampling point, k is 1,2, …, N is the signal length, and the sampling frequency is fs。
2. The method for extracting the characteristic waveform based on the hybrid transformation algorithm according to claim 1, wherein the instantaneous fundamental wave amplitude a (k) is extracted by the following specific process:
s11: carrying out fast Fourier transform on the sampling signals to obtain a sequence X (k);
s12: FFTSHIFT conversion is carried out on the sequence X (k) to obtain a converted sequence Xs(k)；
Xs(k)＝FFTSHIFT(X(k))＝[X(k1),X(k2)]
Wherein k is 1,2, …, N-1, and N is the signal length; FFTSHIFT is a frequency alignment function;
s13: to Xs(k) Filtering to obtain a sequence G (k);
Figure FDA0003381500630000011
wherein f isLFor the cut-off frequency, floor is rounded down, Δ f ═ fsthe/N is a sampling frequency interval;
s14: reducing the frequency component position of the sequence G (k) by adopting FFTSHIFT transformation, and extracting a fundamental wave signal l (k) through fast Fourier inverse transformation;
l(k)＝IFFT(FFTSHIFT(Xs(k)·G(k)))
s15: the instantaneous fundamental wave amplitude value can be extracted by carrying out Hilbert transform on the fundamental wave signal;
a(k)＝abs(HT(l(k)))。
3. the method for extracting the characteristic waveform based on the hybrid transformation algorithm according to claim 1, wherein the singular value characteristic waveform p (k) is extracted by the following specific process:
s21: obtaining a high-frequency signal h (k) according to the fundamental wave signal l (k);
h(k)＝x(k)-l(k)
s22: m continuous data points are selected from the high-frequency signal H (k) to generate a Hankel matrix H with Q rows and M-Q +1 columnsk；
Figure FDA0003381500630000021
Figure FDA0003381500630000022
Wherein q iskA definition vector formed by M data points extracted from the high-frequency signal h (k);
s23: to HkSingular value decomposition is carried out to obtain singular value characteristic waveform p (k):
Figure FDA0003381500630000023
wherein: u shapekAs a diagonal matrix, ΛkAnd
Figure FDA0003381500630000024
orthogonal;
Figure FDA0003381500630000025
Figure FDA0003381500630000026
wherein the content of the first and second substances,
Figure FDA0003381500630000027
is a matrix HkThe characteristic value of (2).
4. A hybrid transform-based system as claimed in claim 1Method for extracting characteristic waveform of algorithm, characterized in that the frequency spectrum X only containing main frequency pointsm(k) The extraction method comprises the following specific processes:
s31: collecting all maximum points in the sequence X (k) to form a maximum sequence | X1(j) 1,2, …, J and J are maximum point numbers;
s32: maximum envelope | X2(k) I is:
Figure FDA0003381500630000028
wherein c is 0, 1, …, dj-1；d1，d2，…，dJFrequency points separated by adjacent extreme points;
Figure FDA0003381500630000029
5. the method for extracting a characteristic waveform based on the hybrid transformation algorithm as claimed in claim 2, wherein in the step S13, a 100Hz low pass filter is adopted for Xs(k) And (6) filtering.
6. The waveform analysis method of the characteristic waveform extracted by the method in the claims 1-5 is characterized in that the instantaneous fundamental wave amplitude a (k) and the singular value characteristic waveform p (k) corresponding to the sampling signal X (k) and the frequency spectrum X only containing the main frequency points are adoptedm(k) And identifying and matching the target signal.
CN202111435045.8A 2021-11-29 2021-11-29 Characteristic waveform extraction method and analysis method based on hybrid algorithm Pending CN114358041A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117118457A (en) * 2023-10-25 2023-11-24 广东电网有限责任公司湛江供电局 Power grid data compression method, system, equipment and medium based on feature extraction

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
王燕: "暂态电能质量扰动检测与识别方法的研究", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》, vol. 2020, no. 03, pages 73 - 80 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117118457A (en) * 2023-10-25 2023-11-24 广东电网有限责任公司湛江供电局 Power grid data compression method, system, equipment and medium based on feature extraction
CN117118457B (en) * 2023-10-25 2024-01-26 广东电网有限责任公司湛江供电局 Power grid data compression method, system, equipment and medium based on feature extraction

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